engibeer/financial-ner-entities-bist30

🏷️ Financial NER Model for Entity Extraction

This is a fine-tuned BERT-based token classification model that extracts structured financial entities from Turkish tweets related to the stock market.

Entities include:

• —🏢 Stock Tickers (e.g., AKBNK, EREGL)
• —🎯 Target Prices (Hedef)
• —📉 Support Prices (Destek)
• —📈 Resistance Prices (Direnç)
• —⏱ Time spans (Vade)
• —📊 Predicted values (Tahmin)

It is a core part of a larger NLP pipeline for parsing and analyzing finance-related predictions on social media.

🧠 Model Details

• —Developed by: damlakonur
• —Model type: BERT fine-tuned for token-classification
• —Language(s): Turkish
• —Finetuned from: bert-base-cased
• —Trained using: Hugging Face Trainer API
• —License: MIT

🚀 How to Use

from transformers import pipeline

model = pipeline(
    "token-classification",
    model="your-username/financial-ner-entities-bist30",
    aggregation_strategy="simple"
)

text = "#AKBNK hedef 60 TL, destek 52 TL."
output = model(text)
print(output)